S6261 VMD+OptiX: Streaming Interactive Ray Tracing from Remote GPU - - PowerPoint PPT Presentation

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S6261 VMD+OptiX: Streaming Interactive Ray Tracing from Remote GPU Clusters to Your VR Headset John E. Stone Theoretical and Computational Biophysics Group Beckman Institute for Advanced Science and Technology University of Illinois at

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NIH BTRC for Macromolecular Modeling and Bioinformatics http://www.ks.uiuc.edu/ Beckman Institute,

  • U. Illinois at Urbana-Champaign

S6261—VMD+OptiX: Streaming Interactive Ray Tracing from Remote GPU Clusters to Your VR Headset

John E. Stone

Theoretical and Computational Biophysics Group Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign http://www.ks.uiuc.edu/

S6261, GPU Technology Conference 10:30-10:55, Room LL20C, San Jose Convention Center, San Jose, CA, Wednesday April 6th, 2016

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NIH BTRC for Macromolecular Modeling and Bioinformatics http://www.ks.uiuc.edu/ Beckman Institute,

  • U. Illinois at Urbana-Champaign

MD Simulations

VMD – “Visual Molecular Dynamics”

Whole Cell Simulation

  • Visualization and analysis of:

– molecular dynamics simulations – particle systems and whole cells – cryoEM densities, volumetric data – quantum chemistry calculations – sequence information

  • User extensible w/ scripting and

plugins

  • http://www.ks.uiuc.edu/Research/vmd/

CryoEM, Cellular Tomography Quantum Chemistry Sequence Data

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NIH BTRC for Macromolecular Modeling and Bioinformatics http://www.ks.uiuc.edu/ Beckman Institute,

  • U. Illinois at Urbana-Champaign

Goal: A Computational Microscope

Study the molecular machines in living cells Ribosome: target for antibiotics Poliovirus

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NIH BTRC for Macromolecular Modeling and Bioinformatics http://www.ks.uiuc.edu/ Beckman Institute,

  • U. Illinois at Urbana-Champaign

Immersive Viz. w/ VMD

  • VMD began as a CAVE app (1993)
  • Use of immersive viz by molecular

scientists limited due to cost, complexity, lack of local availability, convenience

  • Commoditization of HMDs excellent
  • pportunity to overcome cost/availability
  • This leaves many challenges still to solve:

– Incorporate support for remote visualization – UIs, multi-user collaboration/interaction – Rendering perf for large molecular systems – Accomodating limitations idiosynchracies of commercial HMDs

VMD running in a CAVE w/ VR Juggler

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Goal: Intuitive interactive viz. in crowded molecular complexes

Results from 64 M atom, 1 μs sim!

Close-up view of chloride ions permeating through HIV-1 capsid hexameric centers

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NIH BTRC for Macromolecular Modeling and Bioinformatics http://www.ks.uiuc.edu/ Beckman Institute,

  • U. Illinois at Urbana-Champaign

Lighting Comparison

Two lights, no shadows Two lights, hard shadows, 1 shadow ray per light Ambient occlusion + two lights, 144 AO rays/hit

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NIH BTRC for Macromolecular Modeling and Bioinformatics http://www.ks.uiuc.edu/ Beckman Institute,

  • U. Illinois at Urbana-Champaign

1990 1994 1998 2002 2006 2010 104 105 106 107 108 2014 Lysozyme ApoA1 ATP Synthase STMV Ribosome HIV capsid Number of atoms 1986

Computational Biology’s Insatiable Demand for Processing Power

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NIH BTRC for Macromolecular Modeling and Bioinformatics http://www.ks.uiuc.edu/ Beckman Institute,

  • U. Illinois at Urbana-Champaign

HMD Ray Tracing Challenges

  • HMDs require high frame rates (90Hz or more) and minimum latency

between IMU sensor reads and presentation on the display

  • Multi-GPU workstations fast enough to direct-drive HMDs at required

frame rates for simple scenes with direct lighting, hard shadows

  • Advanced RT effects such as AO lighting, depth of field require much

larger sample counts, impractical for direct-driving HMDs

  • Remote viz. required for many HPC problems due to large data
  • Remote viz. latencies too high for direct-drive of HMD
  • Our two-phase approach: moderate-FPS remote RT combined with

local high-FPS view-dependent HMD reprojection w/ OpenGL

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NIH BTRC for Macromolecular Modeling and Bioinformatics http://www.ks.uiuc.edu/ Beckman Institute,

  • U. Illinois at Urbana-Champaign

VMDDisplayList DisplayDevice Tachyon CPU RT

TachyonL-OptiX GPU RT Batch + Interactive

OpenGLDisplayDevice

Di Display play S Subsy ubsystem tem Sce Scene ne Gr Graph ph VMD Molec VMD Molecular ular Str Struc uctu ture e Da Data ta and and Gl Glob

  • bal

al Sta State te Us User In r Inte terf rface Sub Subsy system stem

Tcl/Python Scripting Mouse + Windows VR Input “Tools”

Gr Graphica ical l Rep epresen esenta tation tions

Non-Molecular Geometry DrawMolecule Windowed OpenGL GPU OpenGL Pbuffer GPU FileRenderer

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NIH BTRC for Macromolecular Modeling and Bioinformatics http://www.ks.uiuc.edu/ Beckman Institute,

  • U. Illinois at Urbana-Champaign

Sce Scene ne Gr Graph ph

VMD T VMD Tac achy hyon

  • nL-Opt

OptiX iX Inte Interac activ tive e RT T w/ w/ Op OptiX tiX 3.8 3.8 Pr Prog

  • gres

essiv sive e AP API

RT Pr T Prog

  • gress

essiv ive Sub e Subfr frame ame

rtContextLaunchProgressive2D()

TrBvh rBvh RT Acce T Acceler leration tion Str Struc uctu ture e

rtBu BufferGetPr Progressi ssiveUpdateReady() y()

Draw Output Framebuffer

Check for User Interface Inputs, Update OptiX Variables

rtContextStopProgressive()

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NIH BTRC for Macromolecular Modeling and Bioinformatics http://www.ks.uiuc.edu/ Beckman Institute,

  • U. Illinois at Urbana-Champaign

VMD Scen VMD Scene

VMD T VMD Tac achy hyon

  • nL-Opt

OptiX: iX: Mult Multi-GPU GPU on

  • n NVID

NVIDIA V IA VCA CA Clus Cluste ter

Sc Scene Da Data ta R Repli licate ted, , Ima Image ge Spa Space ce + Samp + Sample le Spa Space ce Par arallel allel Dec Decomp

  • mpositi
  • sition
  • n on
  • nto

to GPU GPUs

VCA 0: 8 K6000 GPUs VCA N: 8 K6000 GPUs

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NIH BTRC for Macromolecular Modeling and Bioinformatics http://www.ks.uiuc.edu/ Beckman Institute,

  • U. Illinois at Urbana-Champaign

VMD 1.9.3 + OptiX 3.8 + CUDA 7.0 ~1.5x Performance Increase

  • OptiX GPU-native “Trbvh” acceleration structure

builder yields substantial perf increase vs. CPU builders running on Opteron 6276 CPUs

  • New optimizations in VMD TachyonL-OptiX RT engine:

– CUDA C++ Template specialization of RT kernels

  • Combinatorial expansion of ray-gen and shading

kernels at compile-time: stereo on/off, AO on/off, depth-of-field on/off, reflections on/off, etc…

  • Optimal kernels selected from expansions at runtime

– Streamlined OptiX context and state management – Optimization of GPU-specific RT intersection routines, memory layout VMD/OptiX GPU Ray Tracing

  • f chromatophore w/ lipids.
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A) Monoscopic circular projection. Eye at center of projection (COP). B) Left eye stereo circular projection. Eye offset from COP by half of interocular distance. C) Stereo eye separation smoothly decreased to zero at zenith and nadir points on the polar axis to prevent incorrect stereo when HMD sees the poles. Zero Eye Sep Zero Eye Sep Full Eye Separation Decreasing Eye Sep Polar Axis Decreasing Eye Sep

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NIH BTRC for Macromolecular Modeling and Bioinformatics http://www.ks.uiuc.edu/ Beckman Institute,

  • U. Illinois at Urbana-Champaign

Stereoscopic Panorama Ray Tracing w/ OptiX

  • Render 360° images and movies for VR

headsets such as Oculus Rift, Google Cardboard

  • Ray trace panoramic stereo spheremaps or

cubemaps for very high-frame-rate display via OpenGL texturing onto simple geometry

  • Stereo requires spherical camera projections

poorly suited to rasterization

  • Benefits from OptiX multi-GPU rendering and

load balancing, remote visualization

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HMD

HMD Display Loop HMD loop runs in main VMD application thread at max OpenGL draw rate View-dependent stereo reprojection for current HMD head pose HMD distortion correction

Camera + Scene

Progressive Ray Tracing Engine Ray tracing loop runs continuously in new thread Decodes H.264 video stream from remote VCA GPU cluster

Remote VCA GPU Cluster Ray tracing runs continuously, streams H.264 video to VMD client

15Mbps Internet Link

Omnistereo Image Stream

VMD

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Scene Per-subframe samples AA : AO (AO per-hit) RT update rate (FPS) STMV shadows 1:0 2:0 4:0 22.2 18.1 10.3 STMV Shadows+AO 1:1 1:2 1:4 18.2 16.1 12.4 STMV Shadows+AO+DoF 1:1 2:1 2:2 16.1 11.1 8.5 HIV-1 Shadows 1:0 2:0 4:0 20.1 18.1 10.2 HIV-1 Shadows+AO 1:1 1:2 1:4 17.4 12.2 8.1

Remote Omnidirectional Stereoscopic RT Performance @ 3072x1536 w/ 2-subframes

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NIH BTRC for Macromolecular Modeling and Bioinformatics http://www.ks.uiuc.edu/ Beckman Institute,

  • U. Illinois at Urbana-Champaign

HMD View-Dependent Reprojection with OpenGL

  • Texture map panoramic image onto reprojection geometry

that matches the original RT image formation surface

  • HMD sees standard perspective frustum view of the

textured surface

  • Commodity HMD optics require software lens distortion

and chromatic aberration correction prior to display, implemented with multi-pass FBO rendering

  • Low-latency, high-frame-rate redraw as HMD head pose

changes (150Hz or more)

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NIH BTRC for Macromolecular Modeling and Bioinformatics http://www.ks.uiuc.edu/ Beckman Institute,

  • U. Illinois at Urbana-Champaign

VMD can support a variety of HMD lens designs, e.g.

http://research.microsoft.com/en-us/um/redmond/projects/lensfactory/oculus/

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NIH BTRC for Macromolecular Modeling and Bioinformatics http://www.ks.uiuc.edu/ Beckman Institute,

  • U. Illinois at Urbana-Champaign
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NIH BTRC for Macromolecular Modeling and Bioinformatics http://www.ks.uiuc.edu/ Beckman Institute,

  • U. Illinois at Urbana-Champaign
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NIH BTRC for Macromolecular Modeling and Bioinformatics http://www.ks.uiuc.edu/ Beckman Institute,

  • U. Illinois at Urbana-Champaign

Future Work

  • Support for more commodity HMDs as they become

generally available

  • Support for OSes besides Linux
  • Ray tracing engine and optimizations:

– Multi-node parallel RT and remote viz. on general clusters and supercomputers, e.g. NCSA Blue Waters, ORNL Titan

– Interactive RT stochastic sampling strategies to improve interactivity – Improved omnidirectional cubemap/spheremap sampling approaches

  • Tons of work to do on VR user interfaces, multi-user

collaborative visualization, …

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NIH BTRC for Macromolecular Modeling and Bioinformatics http://www.ks.uiuc.edu/ Beckman Institute,

  • U. Illinois at Urbana-Champaign

Acknowledgements

  • Theoretical and Computational Biophysics Group, University of Illinois at

Urbana-Champaign

  • Bill Sherman at Indiana University
  • NVIDIA OptiX and CUDA teams
  • NCSA Blue Waters, ORNL OCLF Titan teams
  • NVIDIA CUDA Center of Excellence, University of Illinois at Urbana-

Champaign

  • Funding:

– NSF Blue Waters: NSF OCI 07-25070, PRAC “The Computational Microscope”, ACI-1238993, ACI-1440026 – DOE INCITE, ORNL Titan: DE-AC05-00OR22725 – NIH support: 9P41GM104601, 5R01GM098243-02

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NIH BTRC for Macromolecular Modeling and Bioinformatics http://www.ks.uiuc.edu/ Beckman Institute,

  • U. Illinois at Urbana-Champaign
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Biomedical Technology Research Center for Macromolecular Modeling and Bioinformatics Beckman Institute, University of Illinois at Urbana-Champaign - www.ks.uiuc.edu

Related Publications

http://www.ks.uiuc.edu/Research/gpu/

  • Immersive Molecular Visualization with Omnidirectional Stereoscopic Ray Tracing and Remote
  • Rendering. John E. Stone, William R. Sherman, and Klaus Schulten.High Performance Data Analysis

and Visualization Workshop, IEEE International Parallel and Distributed Processing Symposium Workshop (IPDPSW), 2016. (In-press)

  • High Performance Molecular Visualization: In-Situ and Parallel Rendering with EGL. John E. Stone,

Peter Messmer, Robert Sisneros, and Klaus Schulten.High Performance Data Analysis and Visualization Workshop, IEEE International Parallel and Distributed Processing Symposium Workshop (IPDPSW),

  • 2016. (In-press)
  • Evaluation of Emerging Energy-Efficient Heterogeneous Computing Platforms for Biomolecular

and Cellular Simulation Workloads. John E. Stone, Michael J. Hallock, James C. Phillips, Joseph R. Peterson, Zaida Luthey-Schulten, and Klaus Schulten.25th International Heterogeneity in Computing Workshop, IEEE International Parallel and Distributed Processing Symposium Workshop (IPDPSW),

  • 2016. (In-press)
  • Atomic Detail Visualization of Photosynthetic Membranes with GPU-Accelerated Ray Tracing.
  • J. E. Stone, M. Sener, K. L. Vandivort, A. Barragan, A. Singharoy, I. Teo, J. V. Ribeiro, B. Isralewitz, B. Liu,

B.-C. Goh, J. C. Phillips, C. MacGregor-Chatwin, M. P. Johnson, L. F. Kourkoutis, C. Neil Hunter, and K.

  • Schulten. J. Parallel Computing, 2016. (In-press)
  • Chemical Visualization of Human Pathogens: the Retroviral Capsids. Juan R. Perilla, Boon Chong

Goh, John E. Stone, and Klaus SchultenSC'15 Visualization and Data Analytics Showcase, 2015.

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Biomedical Technology Research Center for Macromolecular Modeling and Bioinformatics Beckman Institute, University of Illinois at Urbana-Champaign - www.ks.uiuc.edu

Related Publications

http://www.ks.uiuc.edu/Research/gpu/

  • Visualization of Energy Conversion Processes in a Light Harvesting Organelle at

Atomic Detail. M. Sener, J. E. Stone, A. Barragan, A. Singharoy, I. Teo, K. L. Vandivort,

  • B. Isralewitz, B. Liu, B. Goh, J. C. Phillips, L. F. Kourkoutis, C. N. Hunter, and K. Schulten.

SC'14 Visualization and Data Analytics Showcase, 2014. ***Winner of the SC'14 Visualization and Data Analytics Showcase

  • Runtime and Architecture Support for Efficient Data Exchange in Multi-Accelerator
  • Applications. J. Cabezas, I. Gelado, J. E. Stone, N. Navarro, D. B. Kirk, and W. Hwu.

IEEE Transactions on Parallel and Distributed Systems, 2014. (In press)

  • Unlocking the Full Potential of the Cray XK7 Accelerator. M. D. Klein and J. E. Stone.

Cray Users Group, Lugano Switzerland, May 2014.

  • GPU-Accelerated Analysis and Visualization of Large Structures Solved by

Molecular Dynamics Flexible Fitting. J. E. Stone, R. McGreevy, B. Isralewitz, and K.

  • Schulten. Faraday Discussions, 169:265-283, 2014.
  • Simulation of reaction diffusion processes over biologically relevant size and time

scales using multi-GPU workstations. M. J. Hallock, J. E. Stone, E. Roberts, C. Fry, and Z. Luthey-Schulten. Journal of Parallel Computing, 40:86-99, 2014.

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Biomedical Technology Research Center for Macromolecular Modeling and Bioinformatics Beckman Institute, University of Illinois at Urbana-Champaign - www.ks.uiuc.edu

Related Publications

http://www.ks.uiuc.edu/Research/gpu/

  • GPU-Accelerated Molecular Visualization on Petascale Supercomputing Platforms.
  • J. Stone, K. L. Vandivort, and K. Schulten. UltraVis'13: Proceedings of the 8th International Workshop
  • n Ultrascale Visualization, pp. 6:1-6:8, 2013.
  • Early Experiences Scaling VMD Molecular Visualization and Analysis Jobs on Blue Waters.
  • J. Stone, B. Isralewitz, and K. Schulten. In proceedings, Extreme Scaling Workshop, 2013.
  • Lattice Microbes: High‐performance stochastic simulation method for the reaction‐diffusion

master equation. E. Roberts, J. Stone, and Z. Luthey‐Schulten.

  • J. Computational Chemistry 34 (3), 245-255, 2013.
  • Fast Visualization of Gaussian Density Surfaces for Molecular Dynamics and Particle System
  • Trajectories. M. Krone, J. Stone, T. Ertl, and K. Schulten. EuroVis Short Papers, pp. 67-71, 2012.
  • Immersive Out-of-Core Visualization of Large-Size and Long-Timescale Molecular Dynamics
  • Trajectories. J. Stone, K. L. Vandivort, and K. Schulten. G. Bebis et al. (Eds.): 7th International

Symposium on Visual Computing (ISVC 2011), LNCS 6939, pp. 1-12, 2011.

  • Fast Analysis of Molecular Dynamics Trajectories with Graphics Processing Units – Radial

Distribution Functions. B. Levine, J. Stone, and A. Kohlmeyer. J. Comp. Physics, 230(9):3556- 3569, 2011.

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Biomedical Technology Research Center for Macromolecular Modeling and Bioinformatics Beckman Institute, University of Illinois at Urbana-Champaign - www.ks.uiuc.edu

Related Publications

http://www.ks.uiuc.edu/Research/gpu/

  • Quantifying the Impact of GPUs on Performance and Energy Efficiency in HPC Clusters.
  • J. Enos, C. Steffen, J. Fullop, M. Showerman, G. Shi, K. Esler, V. Kindratenko, J. Stone,

J Phillips. International Conference on Green Computing, pp. 317-324, 2010.

  • GPU-accelerated molecular modeling coming of age. J. Stone, D. Hardy, I. Ufimtsev,
  • K. Schulten. J. Molecular Graphics and Modeling, 29:116-125, 2010.
  • OpenCL: A Parallel Programming Standard for Heterogeneous Computing.
  • J. Stone, D. Gohara, G. Shi. Computing in Science and Engineering, 12(3):66-73, 2010.
  • An Asymmetric Distributed Shared Memory Model for Heterogeneous Computing
  • Systems. I. Gelado, J. Stone, J. Cabezas, S. Patel, N. Navarro, W. Hwu. ASPLOS ’10:

Proceedings of the 15th International Conference on Architectural Support for Programming Languages and Operating Systems, pp. 347-358, 2010.

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Biomedical Technology Research Center for Macromolecular Modeling and Bioinformatics Beckman Institute, University of Illinois at Urbana-Champaign - www.ks.uiuc.edu

Related Publications

http://www.ks.uiuc.edu/Research/gpu/

  • GPU Clusters for High Performance Computing. V. Kindratenko, J. Enos, G. Shi, M.

Showerman, G. Arnold, J. Stone, J. Phillips, W. Hwu. Workshop on Parallel Programming on Accelerator Clusters (PPAC), In Proceedings IEEE Cluster 2009, pp. 1-8, Aug. 2009.

  • Long time-scale simulations of in vivo diffusion using GPU hardware. E. Roberts, J.

Stone, L. Sepulveda, W. Hwu, Z. Luthey-Schulten. In IPDPS’09: Proceedings of the 2009 IEEE International Symposium on Parallel & Distributed Computing, pp. 1-8, 2009.

  • High Performance Computation and Interactive Display of Molecular Orbitals on GPUs

and Multi-core CPUs. J. Stone, J. Saam, D. Hardy, K. Vandivort, W. Hwu, K. Schulten, 2nd Workshop on General-Purpose Computation on Graphics Pricessing Units (GPGPU-2), ACM International Conference Proceeding Series, volume 383, pp. 9-18, 2009.

  • Probing Biomolecular Machines with Graphics Processors. J. Phillips, J. Stone.

Communications of the ACM, 52(10):34-41, 2009.

  • Multilevel summation of electrostatic potentials using graphics processing units. D.

Hardy, J. Stone, K. Schulten. J. Parallel Computing, 35:164-177, 2009.

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Biomedical Technology Research Center for Macromolecular Modeling and Bioinformatics Beckman Institute, University of Illinois at Urbana-Champaign - www.ks.uiuc.edu

Related Publications

http://www.ks.uiuc.edu/Research/gpu/

  • Adapting a message-driven parallel application to GPU-accelerated clusters.
  • J. Phillips, J. Stone, K. Schulten. Proceedings of the 2008 ACM/IEEE Conference on

Supercomputing, IEEE Press, 2008.

  • GPU acceleration of cutoff pair potentials for molecular modeling applications.
  • C. Rodrigues, D. Hardy, J. Stone, K. Schulten, and W. Hwu. Proceedings of the 2008

Conference On Computing Frontiers, pp. 273-282, 2008.

  • GPU computing. J. Owens, M. Houston, D. Luebke, S. Green, J. Stone, J. Phillips.

Proceedings of the IEEE, 96:879-899, 2008.

  • Accelerating molecular modeling applications with graphics processors. J. Stone, J.

Phillips, P. Freddolino, D. Hardy, L. Trabuco, K. Schulten. J. Comp. Chem., 28:2618-2640, 2007.

  • Continuous fluorescence microphotolysis and correlation spectroscopy. A. Arkhipov, J.

Hüve, M. Kahms, R. Peters, K. Schulten. Biophysical Journal, 93:4006-4017, 2007.